Stable oxygen evolution reaction (OER) catalyst alternatives to the precious IrO₂ catalysts are of great importance to the next-generation proton-exchange membrane (PEM) electrolyzers. RuO₂-based materials are promising candidates but suffer from low stability under highly anodic potentials. Here, we reported a facet-selective etching strategy to improve the stability of polycrystalline RuO₂ without significantly affecting the activity. The selective etching was enabled by the specific chemisorption of pyridazine (pyd) with contingent N atoms onto the RuO₂ surface. The pyd-RuO₂ catalyst, after etching, exhibited a low overpotential 247 mV at 100 mA·cm⁻² and obvious stability improvement of over 200 h at 100 mA·cm⁻² with only 0.63% Ru loss in acidic conditions. Combining various characterization techniques and theoretical calculations, we revealed that the crystalline RuO₂ (110) facet is favorably etched by the coordination of pyridazine while protecting other surfaces, which significantly enriches the RuO₂ (110) facets toward higher OER stability via the dynamic dissolution and repair mechanism in the ordered manner. This study offers alternative perspectives on the dissolution and stability mechanism of RuO₂ and the facet-selective modulation of nanocrystals by ligand-driven etching.

Theoretical simulations of electrocatalysis are vital for understanding the mechanism of the electrochemical process at the atomic level. It can help to reveal the in-situ structures of electrode surfaces and establish the microscopic mechanism of electrocatalysis, thereby solving the problems such as electrode oxidation and corrosion. However, there are still many problems in the theoretical electrochemical simulations, including the solvation effects, the electric double layer, and the structural transformation of electrodes. Here we review recent advances of theoretical methods in electrochemical modeling, in particular, the double reference approach, the periodic continuum solvation model based on the modified Poisson-Boltzmann equation (CM-MPB), and the stochastic surface walking method based on the machine learning potential energy surface (SSW-NN). The case studies of oxygen reduction reaction by using CM-MPB and SSW-NN are presented.